{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification using Stochastic Gradient Descent\n",
    "### Dr. Tirthajyoti Sarkar, Fremont, CA 94536\n",
    "\n",
    "In this notebook, we show the application of Stochastic Gradient Descent for classification problems. **This is particularly useful compared to some other popular classifiers like Random Forest as the training dataset and feature vector size scale up**.\n",
    "\n",
    "We make use of the `SGDClassifier` estimator class from Scikit-learn. Read about it here,\n",
    "<br>https://scikit-learn.org/stable/modules/sgd.html\n",
    "\n",
    "### A bit of theory\n",
    "Stochastic gradient descent (SGD) is an optimization method for unconstrained optimization problems. In contrast to (batch) gradient descent, **SGD approximates the true gradient of  by considering a single training example at a time**. This provides the advantage of speed at the cost of some amount of randomness in the solution trajectory.\n",
    "\n",
    "The class `SGDClassifier` (or `SGDRegressor`) implements a first-order SGD learning routine. The algorithm iterates over the training examples and for each example updates the model parameters according to the update rule given by\n",
    "\n",
    "$$w\\leftarrow w-\\eta\\left (\\alpha \\frac{\\partial R(w)}{\\partial w}+\\frac{\\partial L(w^Tx_i+b,y_i)}{\\partial w} \\right )$$\n",
    "\n",
    "where $\\eta$ is the learning rate which controls the step-size in the parameter space, $x_i$,$y_i$ are the instances of feature and response, respectively, $R(w)$ is the penalty function and $\\alpha$ is the coefficient of the penalty/regularization term. The intercept $b$ is updated similarly but without regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification\n",
    "from time import time\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train using various classifiers with increasing training set size\n",
    "\n",
    "- Create a synthetic dataset (i.e. classification problem) using `make_classification` function\n",
    "- Standardize the feature set using `StandardScaler`\n",
    "- Test/train split\n",
    "- Fit with the training set\n",
    "- Predict and compute accuracy score with the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `SGDClassifier`: Using `hinge` loss (support vector machine loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of the problem:  (1000, 50)\n",
      "Took 15.59 milliseconds\n",
      "Accuracy on the test set: 0.69\n",
      "\n",
      "Size of the problem:  (1584, 50)\n",
      "Took 15.62 milliseconds\n",
      "Accuracy on the test set: 0.746\n",
      "\n",
      "Size of the problem:  (2511, 50)\n",
      "Took 15.622 milliseconds\n",
      "Accuracy on the test set: 0.704\n",
      "\n",
      "Size of the problem:  (3981, 50)\n",
      "Took 15.621 milliseconds\n",
      "Accuracy on the test set: 0.678\n",
      "\n",
      "Size of the problem:  (6309, 50)\n",
      "Took 15.622 milliseconds\n",
      "Accuracy on the test set: 0.763\n",
      "\n",
      "Size of the problem:  (10000, 50)\n",
      "Took 15.617 milliseconds\n",
      "Accuracy on the test set: 0.766\n",
      "\n",
      "Size of the problem:  (15848, 50)\n",
      "Took 17.748 milliseconds\n",
      "Accuracy on the test set: 0.765\n",
      "\n",
      "Size of the problem:  (25118, 50)\n",
      "Took 31.243 milliseconds\n",
      "Accuracy on the test set: 0.77\n",
      "\n",
      "Size of the problem:  (39810, 50)\n",
      "Took 46.831 milliseconds\n",
      "Accuracy on the test set: 0.798\n",
      "\n",
      "Size of the problem:  (63095, 50)\n",
      "Took 78.126 milliseconds\n",
      "Accuracy on the test set: 0.786\n",
      "\n",
      "Size of the problem:  (100000, 50)\n",
      "Took 131.656 milliseconds\n",
      "Accuracy on the test set: 0.817\n",
      "\n",
      "Size of the problem:  (158489, 50)\n",
      "Took 196.583 milliseconds\n",
      "Accuracy on the test set: 0.774\n",
      "\n",
      "Size of the problem:  (251188, 50)\n",
      "Took 320.144 milliseconds\n",
      "Accuracy on the test set: 0.81\n",
      "\n",
      "Size of the problem:  (398107, 50)\n",
      "Took 614.183 milliseconds\n",
      "Accuracy on the test set: 0.81\n",
      "\n",
      "Size of the problem:  (630957, 50)\n",
      "Took 834.651 milliseconds\n",
      "Accuracy on the test set: 0.79\n",
      "\n",
      "Size of the problem:  (1000000, 50)\n",
      "Took 1346.071 milliseconds\n",
      "Accuracy on the test set: 0.809\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sgd_class = SGDClassifier(alpha=0.001,loss='hinge',max_iter=100,tol=0.001,n_jobs=-1,early_stopping=True,n_iter_no_change=2)\n",
    "hinge_acc=[]\n",
    "hinge_time=[]\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "for size in [int(10**(0.2*i)) for i in range(15,31)]:\n",
    "    prob=make_classification(n_samples=size,n_features=50, n_informative=45,n_classes=2,class_sep=0.75,random_state=101)\n",
    "    X,y=prob\n",
    "    X = scaler.fit_transform(X)\n",
    "    print(\"Size of the problem: \",X.shape)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "    t1 = time()\n",
    "    sgd_class.fit(X_train,y_train)\n",
    "    t2 = time()\n",
    "    t_delta=round(1e3*(t2-t1),3)\n",
    "    hinge_time.append(t_delta)\n",
    "    print(f\"Took {t_delta} milliseconds\")\n",
    "    acc = accuracy_score(y_test,sgd_class.predict(X_test))\n",
    "    hinge_acc.append(acc)\n",
    "    print(\"Accuracy on the test set:\", round(acc,3))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `SGDClassifier`: Using `log` loss (logistic regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of the problem:  (1000, 50)\n",
      "Took 15.632 milliseconds\n",
      "Accuracy on the test set: 0.76\n",
      "\n",
      "Size of the problem:  (1584, 50)\n",
      "Took 15.621 milliseconds\n",
      "Accuracy on the test set: 0.687\n",
      "\n",
      "Size of the problem:  (2511, 50)\n",
      "Took 15.589 milliseconds\n",
      "Accuracy on the test set: 0.684\n",
      "\n",
      "Size of the problem:  (3981, 50)\n",
      "Took 0.0 milliseconds\n",
      "Accuracy on the test set: 0.7\n",
      "\n",
      "Size of the problem:  (6309, 50)\n",
      "Took 15.639 milliseconds\n",
      "Accuracy on the test set: 0.73\n",
      "\n",
      "Size of the problem:  (10000, 50)\n",
      "Took 15.605 milliseconds\n",
      "Accuracy on the test set: 0.767\n",
      "\n",
      "Size of the problem:  (15848, 50)\n",
      "Took 15.628 milliseconds\n",
      "Accuracy on the test set: 0.786\n",
      "\n",
      "Size of the problem:  (25118, 50)\n",
      "Took 31.244 milliseconds\n",
      "Accuracy on the test set: 0.778\n",
      "\n",
      "Size of the problem:  (39810, 50)\n",
      "Took 53.865 milliseconds\n",
      "Accuracy on the test set: 0.795\n",
      "\n",
      "Size of the problem:  (63095, 50)\n",
      "Took 78.109 milliseconds\n",
      "Accuracy on the test set: 0.782\n",
      "\n",
      "Size of the problem:  (100000, 50)\n",
      "Took 131.795 milliseconds\n",
      "Accuracy on the test set: 0.818\n",
      "\n",
      "Size of the problem:  (158489, 50)\n",
      "Took 201.432 milliseconds\n",
      "Accuracy on the test set: 0.776\n",
      "\n",
      "Size of the problem:  (251188, 50)\n",
      "Took 345.422 milliseconds\n",
      "Accuracy on the test set: 0.809\n",
      "\n",
      "Size of the problem:  (398107, 50)\n",
      "Took 507.402 milliseconds\n",
      "Accuracy on the test set: 0.808\n",
      "\n",
      "Size of the problem:  (630957, 50)\n",
      "Took 812.97 milliseconds\n",
      "Accuracy on the test set: 0.79\n",
      "\n",
      "Size of the problem:  (1000000, 50)\n",
      "Took 1418.476 milliseconds\n",
      "Accuracy on the test set: 0.809\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sgd_class = SGDClassifier(alpha=0.001,loss='hinge',max_iter=100,tol=0.001,n_jobs=-1,early_stopping=True,n_iter_no_change=2)\n",
    "log_acc=[]\n",
    "log_time=[]\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "for size in [int(10**(0.2*i)) for i in range(15,31)]:\n",
    "    prob=make_classification(n_samples=size,n_features=50, n_informative=45,n_classes=2,class_sep=0.75,random_state=101)\n",
    "    X,y=prob\n",
    "    X = scaler.fit_transform(X)\n",
    "    print(\"Size of the problem: \",X.shape)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "    t1 = time()\n",
    "    sgd_class.fit(X_train,y_train)\n",
    "    t2 = time()\n",
    "    t_delta=round(1e3*(t2-t1),3)\n",
    "    log_time.append(t_delta)\n",
    "    print(f\"Took {t_delta} milliseconds\")\n",
    "    acc = accuracy_score(y_test,sgd_class.predict(X_test))\n",
    "    log_acc.append(acc)\n",
    "    print(\"Accuracy on the test set:\", round(acc,3))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `SGDClassifier`: Using `perceptron` loss/algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of the problem:  (1000, 50)\n",
      "Took 2.959 milliseconds\n",
      "Accuracy on the test set: 0.72\n",
      "\n",
      "Size of the problem:  (1584, 50)\n",
      "Took 4.014 milliseconds\n",
      "Accuracy on the test set: 0.735\n",
      "\n",
      "Size of the problem:  (2511, 50)\n",
      "Took 7.977 milliseconds\n",
      "Accuracy on the test set: 0.703\n",
      "\n",
      "Size of the problem:  (3981, 50)\n",
      "Took 6.982 milliseconds\n",
      "Accuracy on the test set: 0.655\n",
      "\n",
      "Size of the problem:  (6309, 50)\n",
      "Took 9.005 milliseconds\n",
      "Accuracy on the test set: 0.753\n",
      "\n",
      "Size of the problem:  (10000, 50)\n",
      "Took 15.618 milliseconds\n",
      "Accuracy on the test set: 0.768\n",
      "\n",
      "Size of the problem:  (15848, 50)\n",
      "Took 31.211 milliseconds\n",
      "Accuracy on the test set: 0.778\n",
      "\n",
      "Size of the problem:  (25118, 50)\n",
      "Took 15.622 milliseconds\n",
      "Accuracy on the test set: 0.772\n",
      "\n",
      "Size of the problem:  (39810, 50)\n",
      "Took 46.863 milliseconds\n",
      "Accuracy on the test set: 0.799\n",
      "\n",
      "Size of the problem:  (63095, 50)\n",
      "Took 73.802 milliseconds\n",
      "Accuracy on the test set: 0.788\n",
      "\n",
      "Size of the problem:  (100000, 50)\n",
      "Took 120.57 milliseconds\n",
      "Accuracy on the test set: 0.818\n",
      "\n",
      "Size of the problem:  (158489, 50)\n",
      "Took 201.719 milliseconds\n",
      "Accuracy on the test set: 0.777\n",
      "\n",
      "Size of the problem:  (251188, 50)\n",
      "Took 444.183 milliseconds\n",
      "Accuracy on the test set: 0.813\n",
      "\n",
      "Size of the problem:  (398107, 50)\n",
      "Took 591.417 milliseconds\n",
      "Accuracy on the test set: 0.809\n",
      "\n",
      "Size of the problem:  (630957, 50)\n",
      "Took 904.98 milliseconds\n",
      "Accuracy on the test set: 0.79\n",
      "\n",
      "Size of the problem:  (1000000, 50)\n",
      "Took 1325.461 milliseconds\n",
      "Accuracy on the test set: 0.809\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sgd_class = SGDClassifier(alpha=0.001,loss='hinge',max_iter=100,tol=0.001,n_jobs=-1,early_stopping=True,n_iter_no_change=2)\n",
    "perceptron_acc=[]\n",
    "perceptron_time=[]\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "for size in [int(10**(0.2*i)) for i in range(15,31)]:\n",
    "    prob=make_classification(n_samples=size,n_features=50, n_informative=45,n_classes=2,class_sep=0.75,random_state=101)\n",
    "    X,y=prob\n",
    "    X = scaler.fit_transform(X)\n",
    "    print(\"Size of the problem: \",X.shape)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "    t1 = time()\n",
    "    sgd_class.fit(X_train,y_train)\n",
    "    t2 = time()\n",
    "    t_delta=round(1e3*(t2-t1),3)\n",
    "    perceptron_time.append(t_delta)\n",
    "    print(f\"Took {t_delta} milliseconds\")\n",
    "    acc = accuracy_score(y_test,sgd_class.predict(X_test))\n",
    "    perceptron_acc.append(acc)\n",
    "    print(\"Accuracy on the test set:\", round(acc,3))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest classifier\n",
    "We set the number of decision trees (`n_estimators`) to 20 and `max_depth` to 5, to keep the individual trees simple and shallow. Please feel free to experiment with these values to see if that improves the computation efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of the problem:  (1000, 50)\n",
      "Took 140.594 milliseconds\n",
      "Accuracy: 0.76\n",
      "\n",
      "Size of the problem:  (1584, 50)\n",
      "Took 124.964 milliseconds\n",
      "Accuracy: 0.752\n",
      "\n",
      "Size of the problem:  (2511, 50)\n",
      "Took 124.966 milliseconds\n",
      "Accuracy: 0.755\n",
      "\n",
      "Size of the problem:  (3981, 50)\n",
      "Took 125.091 milliseconds\n",
      "Accuracy: 0.783\n",
      "\n",
      "Size of the problem:  (6309, 50)\n",
      "Took 125.122 milliseconds\n",
      "Accuracy: 0.801\n",
      "\n",
      "Size of the problem:  (10000, 50)\n",
      "Took 112.7 milliseconds\n",
      "Accuracy: 0.821\n",
      "\n",
      "Size of the problem:  (15848, 50)\n",
      "Took 113.889 milliseconds\n",
      "Accuracy: 0.804\n",
      "\n",
      "Size of the problem:  (25118, 50)\n",
      "Took 214.046 milliseconds\n",
      "Accuracy: 0.824\n",
      "\n",
      "Size of the problem:  (39810, 50)\n",
      "Took 346.047 milliseconds\n",
      "Accuracy: 0.831\n",
      "\n",
      "Size of the problem:  (63095, 50)\n",
      "Took 453.022 milliseconds\n",
      "Accuracy: 0.802\n",
      "\n",
      "Size of the problem:  (100000, 50)\n",
      "Took 818.018 milliseconds\n",
      "Accuracy: 0.83\n",
      "\n",
      "Size of the problem:  (158489, 50)\n",
      "Took 1233.369 milliseconds\n",
      "Accuracy: 0.811\n",
      "\n",
      "Size of the problem:  (251188, 50)\n",
      "Took 2009.192 milliseconds\n",
      "Accuracy: 0.792\n",
      "\n",
      "Size of the problem:  (398107, 50)\n",
      "Took 3374.022 milliseconds\n",
      "Accuracy: 0.815\n",
      "\n",
      "Size of the problem:  (630957, 50)\n",
      "Took 5693.593 milliseconds\n",
      "Accuracy: 0.812\n",
      "\n",
      "Size of the problem:  (1000000, 50)\n",
      "Took 9441.015 milliseconds\n",
      "Accuracy: 0.809\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=20,max_depth=5,min_samples_leaf=5,min_samples_split=10,n_jobs=-1)\n",
    "rf_acc=[]\n",
    "rf_time=[]\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "for size in [int(10**(0.2*i)) for i in range(15,31)]:\n",
    "    prob=make_classification(n_samples=size,n_features=50, n_informative=45,n_classes=2,class_sep=0.75,random_state=101)\n",
    "    X,y=prob\n",
    "    X = scaler.fit_transform(X)\n",
    "    print(\"Size of the problem: \",X.shape)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "    t1 = time()\n",
    "    rf.fit(X_train,y_train)\n",
    "    t2 = time()\n",
    "    t_delta=round(1e3*(t2-t1),3)\n",
    "    rf_time.append(t_delta)\n",
    "    print(f\"Took {t_delta} milliseconds\")\n",
    "    acc = accuracy_score(y_test,rf.predict(X_test))\n",
    "    rf_acc.append(acc)\n",
    "    print(\"Accuracy:\", round(acc,3))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_var(var,var_name):\n",
    "    size =np.array([int(10**(0.2*i)) for i in range(15,31)])\n",
    "    plt.figure(figsize=(8,5))\n",
    "    plt.title(f\"{var_name} with training set size\",fontsize=18)\n",
    "    plt.semilogx(size*0.7,var,marker='o',color='k',lw=3,markersize=12)\n",
    "    plt.grid(True)\n",
    "    plt.xticks(fontsize=15)\n",
    "    plt.yticks(fontsize=15)\n",
    "    plt.xlabel(\"Training set size\",fontsize=15)\n",
    "    plt.ylabel(\"Training time (milliseconds)\",fontsize=15)\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var(hinge_time,'Hinge loss time')\n",
    "plot_var(log_time,'Logistic loss time')\n",
    "plot_var(perceptron_time,'Perceptron algorithm time')\n",
    "plot_var(rf_time,'Random Forest time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var(hinge_acc,'Hinge loss accuracy')\n",
    "plot_var(log_acc,'Logistic loss accuracy')\n",
    "plot_var(perceptron_acc,'Perceptron algorithm accuracy')\n",
    "plot_var(rf_acc,'Random Forest accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation\n",
    "\n",
    "While achieving similar accuracy level, the `SGDClassifier` estimator variants demonstrate faster training time as compared to the Random Forest classifier. The difference is not that significant at the low end of the training set size (< 100,000). But the difference becomes prominent for larger training set size."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
